Designing digital services with cryptographic guarantees for data security and privacy

In the past two decades, tremendously successful digital services have been built that collect, process, and monetize massive amounts of personal user data, up to the point where data is proclaimed the oil of the 21st century. Along come serious threats to data security and privacy that significantly increase the demand for effective protection, e.g., as manifested in the growth of encrypted Internet traffic. Communication security protocols, however, protect data against external attackers and do not address the root cause of almost all privacy threats, the need to share sensitive data with third parties. These third parties may illicitly process data beyond its original purpose of collection or be hacked and forced to provide data access. Countering these threats requires the development of Privacy Enhancing Technologies that complement or replace traditional communication security protocols. We identify Secure Multiparty Computation (SMC) as a rigorous approach not only to provide data security and privacy protection, but even to reconcile privacy interests with seemingly adverse public and business interests. However, the potential of SMC is foremost on the theoretical level - it is often dismissed for being too inefficient and impedimentary for real-world applications. This thesis bridges the gap between the theoretical strength of SMC and the feeble realization of its potential in practice. To this end, we conduct a qualitative and quantitative analysis of SMC frameworks and abstract three research challenges: i) Extending the functionality and ii) increasing the efficiency of SMC as well as iii) customizing it to challenged environments. We choose a use case-driven research methodology to address these questions, which allows us to motivate and validate all our contributions in practice. First, we motivate the problem of financial privacy in cryptocurrencies and propose decentralized mixing as a solution. We recognize the advantages of securing mixing operations with SMC and contribute secure protocols to technically realize our novel approach. As a result, our mixing system achieves stronger security and privacy guarantees than prior works while remaining highly scalable and fully compatible with the prevalent designs of decentralized cryptocurrencies such as Bitcoin. Second, we propose efficient SMC designs for different classification algorithms to address data security and privacy issues in pattern recognition and machine learning. The evaluation of our classifiers shows that they are secure, accurate, and outperform the state of the art. We demonstrate three real-world use cases that prove applicability of our classifiers but also motivate their deployment in challenged environments. Thus, we present two additional approaches, bandwidth optimizations and secure outsourcing, to bring our secure classifiers to these scenarios. Finally, we investigate secure outsourcing as a general strategy to customize SMC to challenged deployment and operation scenarios by the example of computing set intersections, a universal building block in many real-world applications and a well studied SMC problem. We present efficient schemes with negligible overheads for the outsourcers and demonstrate their applicability in two comprehensive case studies, privacy-preserving crowd-sensing and genetic disease testing in the cloud. In summary, the contributions made in this thesis widen the technical solution space for practical data security and privacy protection in data-driven digital services